The present disclosure relates generally to manufacturing. Specifically, the present disclosure relates to systems and methods that use multiple defect tests to eliminate false defect identifications.
As time goes on, semiconductor devices generally get smaller, with more transistors and other structures on a single die. As the structures get smaller, testing intermediate tools for making the products and testing the products themselves becomes more challenging. For instance, some photomasks are produced using electron beam writing to shape very fine features onto the masks, where examples of very fine features include Optical Proximity Correction (OPC) features. A conventional testing technique uses a single optical testing recipe that looks at both transmitted and reflected light from the mask and discerns where defects exist in the mask. As the features of masks get smaller, false defect detections typically increase.
In some conventional systems, a first defect detection recipe is created with a baseline sensitivity and then applied to a mask with a known geometry and known defects. The results are examined to see whether the recipe detected all real defects and to see how many false defects were detected. The sensitivity may be adjusted up if not all real defects were detected and may be adjusted down if there was a very high incidence of false detections. The process can be iterative to adjust the sensitivity to a point where all real defects are detected and false defects are minimized as much as possible. Several other recipes may be created and tuned as well.
Some recipes use test processes that are more sensitive to light reflected from a mask while other recipes use test processes that are more sensitive to light transmitted through a mask. Additionally, some testing recipes produce more false defect detections than do other testing recipes. Thus, choosing a single testing recipe for a mask in production can be important since it affects the number of defects, whether real or false, that are detected.
Typically, after defects are detected, a human user goes through the identified defects and discerns which of the defects are real and which are false. Real defects are then repaired before the mask is finished and shipped to a customer.
Choosing a recipe that produces an excessive number of false detections may unnecessarily increase cycle time and cost money. An efficient way to reduce false defect detection is called for.